Localization of the Optical Phonon Modes in Boron Nitride Nanotubes: Mixing Effect of <sup>10</sup>B Isotopes and Vacancies
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We explored the mixing effect of 10 B isotopes and boron (B) or nitrogen (N) vacancies on the atomic vibrational properties of (10,0) single-wall boron nitride nanotubes (BNNTs). The forced oscillation technique was employed to evaluate the phonon modes for the entire range (0–100%) of 10 B isotopes and atomic vacancy densities ranging from 0 to 30%. With increasing isotope densities, we noticed a blue shift of the Raman-active A 1 phonon peak, whereas an increased density of mixed or independent B and N vacancies resulted in the emergence of a new low-frequency peak and the annihilation of the A 1 peak in the phonon density of states. High-energy optical phonons were localized as a result of both 10 B isotopes and the presence of mixing defects. We found an asymmetrical nature of the localization length with increasing 10 B isotope content, which corresponds well to the isotope-inherited localization length of carbon nanotubes and monolayer graphene. The localization length falls abruptly with the increase in concentration of both atomic vacancies (B or N) and mixing defects ( 10 B isotope and vacancies). These findings are critical for understanding heat conduction and nanoscopic vibrational investigations such as tip-enhanced Raman spectra in BNNTs, which can map local phonon energies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".